Clinical Feasibility of Brain Radiotherapy Using Synthetic CTs in an MRI-only Workflow

Trial Title: Clinical Feasibility of Brain Radiotherapy Using Synthetic CTs in an MRI-only Workflow

NCT ID: NCT06106997

Condition: Brain Tumor, Primary
Brain Tumor - Metastatic

Conditions: Official terms:
Brain Neoplasms

Study type: Interventional

Study phase: N/A

Overall status: Not yet recruiting

Study design:

Allocation: N/A

Intervention model: Single Group Assignment

Primary purpose: Other

Masking: None (Open Label)

Intervention:

Intervention type: Radiation
Intervention name: brain radiotherapy
Description: brain radiotherapy planned on synthetic CTs
Arm group label: sCT workflow

Summary: The goal of this observational study is to show the feasibility of an MRI-only workflow in brain radiotherapy. The main question it aims to answer is: - Is an MRI-only workflow based on deep learning sCTs feasible in clinical routine? Participants will be treated as in clinical routine, but treatment planning will be based on sCTs, that are generated from MRI images. The dosimetrical equivalence to the standard CT based workflow will be tested at several points in the study.

Detailed description: The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data. A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.

Criteria for eligibility:
Criteria:
Inclusion Criteria: - Written informed consent - Patient older than 18 years - Tumor or metastases in the brain - Immobilization with stereotactic mask - Treatment on stereotactic linear accelerator (2.5 mm leafs) equipped with 2D/2D X-ray system Exclusion Criteria: - Metal in the body, metal implants, pacemakers or other patient-specific factors that are a contraindication to an MRI scan - Metal implants, pacemakers or other patient-specific factors associated with increased risk from an MRI scan - Renal insufficiency (eGFR < 60 ml/min), allergy or other patient-specific factors that constitute a contraindication to contrast administration - Renal insufficiency (eGFR < 60 ml/min), allergy or other patient-specific factors associated with an increased risk from contrast administration - Patients who are institutionalized in care facilities, prisons or other supervised facilities. - Patients under guardianship - Non-consenting patients - Pregnant or breastfeeding patients

Gender: All

Minimum age: 18 Years

Maximum age: N/A

Healthy volunteers: No

Locations:

Facility:
Name: Erlangen, Universitätsklinikum Strahlenklinik

Address:
City: Erlangen
Zip: 91054
Country: Germany

Start date: January 2024

Completion date: August 2025

Lead sponsor:
Agency: University of Erlangen-Nürnberg Medical School
Agency class: Other

Source: University of Erlangen-Nürnberg Medical School

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06106997